Relation-aware Ensemble Learning for Knowledge Graph Embedding
Ling Yue, Yongqi Zhang, Quanming Yao, Yong Li, Xian Wu, Ziheng Zhang, Zhenxi Lin, Yefeng Zheng
Abstract
Knowledge graph (KG) embedding is a fundamental task in natural language processing, and various methods have been proposed to explore semantic patterns in distinctive ways. In this paper, we propose to learn an ensemble by leveraging existing methods in a relation-aware manner. However, exploring these semantics using relation-aware ensemble leads to a much larger search space than general ensemble methods. To address this issue, we propose a divide-search-combine algorithm RelEns-DSC that searches the relation-wise ensemble weights independently. This algorithm has the same computation cost as general ensemble methods but with much better performance. Experimental results on benchmark datasets demonstrate the effectiveness of the proposed method in efficiently searching relation-aware ensemble weights and achieving state-of-the-art embedding performance. The code is public at https://github.com/LARS-research/RelEns.- Anthology ID:
- 2023.emnlp-main.1034
- Volume:
- Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
- Month:
- December
- Year:
- 2023
- Address:
- Singapore
- Editors:
- Houda Bouamor, Juan Pino, Kalika Bali
- Venue:
- EMNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 16620–16631
- Language:
- URL:
- https://aclanthology.org/2023.emnlp-main.1034
- DOI:
- 10.18653/v1/2023.emnlp-main.1034
- Cite (ACL):
- Ling Yue, Yongqi Zhang, Quanming Yao, Yong Li, Xian Wu, Ziheng Zhang, Zhenxi Lin, and Yefeng Zheng. 2023. Relation-aware Ensemble Learning for Knowledge Graph Embedding. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 16620–16631, Singapore. Association for Computational Linguistics.
- Cite (Informal):
- Relation-aware Ensemble Learning for Knowledge Graph Embedding (Yue et al., EMNLP 2023)
- PDF:
- https://preview.aclanthology.org/emnlp22-frontmatter/2023.emnlp-main.1034.pdf